code: def _compare_with_sklearn_mse_regressor_iris(
      n_estimators=99, sample_weight=None, description=""
  ):
      x_train, x_test, y_train, y_test = train_test_split(
          IRIS.data, IRIS.target, test_size=0.33, random_state=31
      )
  
      scikit_model = ScikitRandomForestRegressor(
          n_estimators=n_estimators, max_depth=None, random_state=777
      )
      daal4py_model = DaalRandomForestRegressor(
          n_estimators=n_estimators, max_depth=None, random_state=777
      )
  
      scikit_predict = scikit_model.fit(
          x_train, y_train, sample_weight=sample_weight
      ).predict(x_test)
      daal4py_predict = daal4py_model.fit(
          x_train, y_train, sample_weight=sample_weight
      ).predict(x_test)
  
      scikit_mse = mean_squared_error(scikit_predict, y_test)
      daal4py_mse = mean_squared_error(daal4py_predict, y_test)
  
      ratio = daal4py_mse / scikit_mse
      reason = (
          description
          + f"scikit_mse={scikit_mse}, daal4py_mse={daal4py_mse}"
      )
      assert ratio <= MSE_RATIO, reason
  
file_path: scikit-learn-intelex/daal4py/sklearn/ensemble/tests/test_decision_forest.py
imports: import random
  import numpy
  import pytest
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.metrics import accuracy_score, log_loss, mean_squared_error, roc_auc_score
  from sklearn.model_selection import train_test_split
  from daal4py.sklearn._utils import daal_check_version
  from daal4py.sklearn.ensemble import RandomForestClassifier
  from daal4py.sklearn.ensemble import RandomForestRegressor
label_desc: Use image node_id 5 for calling a global function with example usage: _compare_with_sklearn_mse_regressor_iris(n_estimators, sample_weight, description) without return types